{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T08:15:46Z","timestamp":1768983346723,"version":"3.49.0"},"reference-count":22,"publisher":"Emerald","issue":"1","license":[{"start":{"date-parts":[[2023,5,3]],"date-time":"2023-05-03T00:00:00Z","timestamp":1683072000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["DTA"],"published-print":{"date-parts":[[2024,1,29]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>Traffic flow prediction has always been a top priority of intelligent transportation systems. There are many mature methods for short-term traffic flow prediction. However, the existing methods are often insufficient in capturing long-term spatial-temporal dependencies. To predict long-term dependencies more accurately, in this paper, a new and more effective traffic flow prediction model is proposed.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>This paper proposes a new and more effective traffic flow prediction model, named channel attention-based spatial-temporal graph neural networks. A graph convolutional network is used to extract local spatial-temporal correlations, a channel attention mechanism is used to enhance the influence of nearby spatial-temporal dependencies on decision-making and a transformer mechanism is used to capture long-term dependencies.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The proposed model is applied to two common highway datasets: METR-LA collected in Los Angeles and PEMS-BAY collected in the California Bay Area. This model outperforms the other five in terms of performance on three performance metrics a popular model.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>(1) Based on the spatial-temporal synchronization graph convolution module, a spatial-temporal channel attention module is designed to increase the influence of proximity dependence on decision-making by enhancing or suppressing different channels. (2) To better capture long-term dependencies, the transformer module is introduced.<\/jats:p><\/jats:sec>","DOI":"10.1108\/dta-09-2022-0378","type":"journal-article","created":{"date-parts":[[2023,5,3]],"date-time":"2023-05-03T08:36:26Z","timestamp":1683102986000},"page":"81-94","source":"Crossref","is-referenced-by-count":4,"title":["Channel attention-based spatial-temporal graph neural networks for traffic prediction"],"prefix":"10.1108","volume":"58","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4054-6385","authenticated-orcid":false,"given":"Bin","family":"Wang","sequence":"first","affiliation":[]},{"given":"Fanghong","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Le","family":"Tong","sequence":"additional","affiliation":[]},{"given":"Qian","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Sulei","family":"Zhu","sequence":"additional","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2023,5,3]]},"reference":[{"issue":"3","key":"key2024012913143320000_ref001","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1111\/tgis.12644","article-title":"Traffic transformer: capturing the continuity and periodicity of time series for traffic forecasting","volume":"24","year":"2020","journal-title":"Transactions in GIS"},{"key":"key2024012913143320000_ref002","article-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","year":"2014","journal-title":"arXiv preprint arXiv:1412.3555"},{"issue":"2","key":"key2024012913143320000_ref003","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1207\/s15516709cog1402_1","article-title":"Finding structure in time","volume":"14","year":"1990","journal-title":"Cognitive Science"},{"key":"key2024012913143320000_ref004","doi-asserted-by":"crossref","unstructured":"Graves, A. 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